How to Select a Target Meal

Abstract

The present invention relates to a method for selecting a diet for a subject based on health data, the method comprising determining two or more markers of two or more metabolic pathway clusters (i.e., carbohydrate metabolism, lipid metabolism, inflammation, microbiota metabolism, oxidative stress) in an isolated sample of the subject; then, a score is given to each cluster, and a diet is recommended taking into account the value of the score. The present invention also relates to a system for implementing this method.

Description

[Technical Field]

[0001] This application claims the benefit of European Patent Application No. 22382965.6, filed October 11, 2022.

[0002] The present invention is framed in the field of nutrition and health science, a method for making appropriate dietary recommendations to a subject, in which several data of the subject are taken into account, as well as a system for carrying out the method. [Background technology]

[0003] Personalized nutrition (PN) has attracted significant attention over the past decade and is currently one of the top trends in nutrition science. This field offers the possibility of adapting dietary behavior to individual needs and preferences, primarily to optimize benefits. From the consumer's perspective, PN represents a natural pathway to empowerment and facilitates decision-making processes that affect very different areas of life, such as physical activity performance, mental and physical well-being, or overall health improvement. Furthermore, PN is becoming increasingly relevant from a healthcare system perspective, as a wide range of highly prevalent non-communicable diseases that place a significant burden on healthcare systems are directly linked to dietary patterns and behaviors. A well-known example is obesity and obesity-related diseases, which are closely linked to imbalanced behaviors in terms of physical activity, psychological distress, and inappropriate eating patterns. Unfortunately, general nutrition recommendations, even those adopted by national and international governments, have repeatedly proven insufficient to promote necessary changes in individuals' eating behaviors. Perhaps PN will be most successful if it specifically addresses metabolic abnormalities that can be nutritionally targeted through specific dietary choices, with the results measured and made visible to consumers. Specific genetically determined susceptibilities and risks may be taken into account in this process.

[0004] Metabotyping (establishing an individual's personal metabolic profile) allows for classification of individuals according to their metabolic signature, which can be related to health status, dietary patterns, and response to interventions. Nevertheless, although metabolic markers can now be applied to obtain an accurate overview of a person's metabolic state at the metabolic level, nutritional recommendations are still far from the precision required to meet the specific requirements of every person. This apparent gap is the result of a lack of knowledge regarding the effects of individual eating behaviors on specific components of metabolism, as the complexity of food itself is further confounded by the complex processes of digestion, absorption, and metabolism, as well as the interactions and signaling properties of food components, which depend on individual intrinsic and environmental factors.

[0005] European Patent No. 3529379 discloses a system and method for implementing dietary selection based on a subject's vitals, genotype, and phenotype, in which a subject's metabolic fitness is determined by analyzing the user's blood after consumption of a multinutrient-loaded beverage, the multinutrient-loaded beverage containing: a) 44 to 57 grams of total fat; b) 75±15 grams of total carbohydrate; and c) 20±3 grams of total protein. Insulin, glucose, and triglycerides are determined from the blood sample. The authors demonstrate that this system and method, combined with the subject's eating preferences, allows for the appropriate selection of dietary components. However, these methods and systems require prior beverage consumption, which complicates implementation. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] European Patent No. 3529379 Summary of the Invention [Problem to be solved by the invention]

[0007] Overall, there remains a need for additional methods of nutritional recommendations that comprehensively reflect a person's personality and goals. [Means for solving the problem]

[0008] The inventors have determined an advantageous manner of selecting a subject for a given diet, or in other words, a way of recommending a particular diet to a subject from the analysis of certain markers, including metabolic informative markers, and optionally other characteristics of the subject, such as specific genotypes or physical parameters (i.e., body mass index, race, sex, age, etc.).

[0009] Such a selection method is of particular interest to healthy subjects who wish to follow or know the most appropriate diet according to their characteristics, and thus is useful in the case of certain diseases, but also makes it possible to maintain, or perhaps maintain or preserve, a state of health in the absence of disease or in the event of a declared disease.

[0010] As can be seen in the examples, the method of the present invention allows for the selection of an appropriate diet, which ultimately leads to the modulation of markers indicative of the diet to be selected for the benefit of the subject. Thus, this method is directly applicable to the selection of an effective diet.

[0011] This method goes beyond the routine control analysis currently performed to determine whether a certain organ or system is within an established range of normal operation or function, and also allows professionals to administer medical regimens and / or dietary recommendations. The method of the present invention takes a step forward in that certain markers of different information clusters are determined in isolated samples, and then their presence, level, or absence is assigned a value that is taken into account to make a decision in conjunction with the marker values ​​obtained for other clusters. Thus, the analysis is performed taking into account the values ​​associated with all analyzed metabolic information clusters.

[0012] To the inventors' knowledge, this is the first time that a group of proposed markers of a certain cluster have been brought together for the purposes shown, and also the first time that several markers of a cluster have been calculated together to provide relevant information regarding the optimal diet for a subject.

[0013] Accordingly, a first aspect of the present invention is a method of selecting a diet for a subject based on health data, comprising the steps of: (i) in an isolated sample of a subject, two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress determining two or more markers of (ii) assigning each cluster a score derived from a function that takes into account the levels and / or concentrations of two or more markers in the sample; (iii) comparing the scores obtained for clusters (a) to (e) to determine the gradation between them; (iv) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score value; The method includes:

[0014] Thus, for example, after carrying out the method of the first aspect, a diet that fits the metabolic cluster with the highest score is recommended to the subject, which selection reduces the score of said cluster within a certain period of time, as can be seen in the examples.

[0015] If the highest score is for carbohydrate metabolism, this means that the metabolic pathway of carbohydrate metabolism is most likely to induce or cause a nutritional disorder or disease associated with metabolic disorder.

[0016] The scores of all analyzed clusters are used to recommend a diet that modulates at least one of the cluster scores.

[0017] In another example, if one of the analyzed clusters gives a score outside the reference interval or above or below the cutoff value for that score, a diet is recommended to adjust the score to the reference or to bring it closer to that cutoff value.

[0018] Those skilled in the art will appreciate that the essence of the present invention lies in the combination of different biomarkers (i.e., at least two) that define a metabolic signature, each marker associated with a slightly different aspect of the same signature. In other words, the essence lies in considering or converting the different biomarkers into a single composite biomarker (i.e., mathematically converted into a score for a particular cluster).

[0019] Thus, the present method proposes a procedure for determining personalized nutritional recommendations based on information obtained from metabolomic clusters, where in some embodiments each (bio)marker within a cluster is weighted according to their relative significance within the cluster.

[0020] Without being bound by any theory, the rationale behind the effectiveness of this method is, at least in part, that dysregulation of global processes (i.e., holistic and complex processes that control health status) manifests as measurable changes in distinct components of metabolism under fixed conditions, such as overnight fasting. The metabolic components that likely reflect five global processes are lipid metabolism, carbohydrate metabolism, systemic inflammation (i.e., inflammation), oxidative stress, and microbiota status (i.e., microbiota metabolism). Because sustained changes in these five components are associated with the likelihood of developing different diseases, they are also referred to herein as "core health processes."

[0021] A key feature of the core health processes is that they can be thought of as independent clusters of distinct metabolites and proteins, each of which recapitulates distinct and complementary aspects of metabolism and is currently recognized as an established clinical biomarker or is in advanced research (i.e., meta-analysis of clinical trials) as an indicator of a specific condition or metabolic change. These novel integrated biomarkers, combined by algorithms assisted by machine learning techniques, surprisingly provided a measure of the state of each core health process. The main advantage of this approach is that each core health process is composed of a combination of different biomarkers (i.e., at least two) that comprise a health signature, with each marker associated with a slightly different aspect of the same signature. Therefore, the information provided by these biomarkers regarding physiology and health-to-disease progression is complementary. For example, combining these biomarkers by considering both health-to-disease progression and the association of blood concentrations could more sensitively capture changes in inflammatory homeostasis. Therefore, we propose that subtle, undetectable changes in metabolic processes when the biomarkers are considered separately become detectable when the different biomarkers are considered as a single composite biomarker. At the same time, significant changes in a single but relevant biomarker of the signature do not affect detection ability, despite the unlikely situation where other biomarkers remain unchanged.

[0022] As will be shown, machine learning and / or specially developed systems for carrying out the method allow its best implementation.

[0023] Thus, a second aspect of the present invention provides: Two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress a database storing data of two or more markers determined in an isolated sample of a subject; a memory storing program instructions including program instructions capable of implementing: (i) a function for obtaining a metabolic cluster score from the levels and / or concentrations of two or more markers in a sample; (ii) a comparison of the scores to determine a gradation between the scores; and (iii) a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; a processor coupled to the database and memory that, when executing the program instructions, causes a function to obtain a metabolic cluster score from the levels and / or concentrations of two or more markers in the sample, compare the scores to determine a gradient therebetween, and make a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; A system for selecting a target diet based on health data, comprising: DETAILED DESCRIPTION OF THE INVENTION

[0024] All terms used herein in this application shall be understood to have their ordinary meaning as known in the art unless otherwise specified. Other, more specific definitions of certain terms used in this application are as follows, and are intended to be applied uniformly throughout the specification and claims, unless a definition expressly set forth otherwise provides a broader definition.

[0025] As used herein, the indefinite articles "a" and "an" are synonymous with "at least one" or "one or more." Unless otherwise indicated, definite articles such as "the" used herein also include the plural of the noun.

[0026] For purposes of the present invention, any range given includes both the lower and upper endpoints of the range.

[0027] The term "marker" as used herein includes either the level of a substance (i.e., a metabolite, carbohydrate, lipid, protein, peptide, mRNA, miRNA, etc.) in an isolated sample, or the presence of a certain mutation, or genotype such as a single nucleotide polymorphism (SNP), or other condition, such as a certain disease.

[0028] The term "carbohydrate metabolism" refers to the entire biochemical process responsible for the metabolic formation, degradation, and interconversion of carbohydrates in living organisms. The metabolic pathways included under the umbrella of carbohydrate metabolism are glycolysis, gluconeogenesis, glycogen synthesis, pentose phosphate pathway, fructose metabolism, and galactose metabolism. The metabolic control elements of these metabolic pathways, such as insulin, adiponectin, leptin, HOMA-IR, and certain amino acids, among others, are also included under the umbrella of carbohydrate metabolism. Those skilled in the art will be able to identify all of these pathways and the enzymes, receptors, and compounds involved therein. Therefore, the term "carbohydrate metabolism cluster" or "carbohydrate metabolism cluster" as used herein refers to a group of markers (i.e., glucose, glutamate, insulin, leptin, etc.) that are known to provide information about the current status of all biochemical and cellular events involved in carbohydrate metabolism at a specific moment in a subject's life.

[0029] The term "lipid metabolism" refers to the synthesis and degradation of lipids in cells, including the breakdown or storage of fat for energy, and the synthesis of structural and functional lipids, such as those involved in the construction of cell membranes.It includes biochemical pathways known as "lipid digestion", lipid absorption, lipid transport, lipid storage, lipid catabolism, and lipid biosynthesis.Those skilled in the art will be able to identify all of these pathways and the enzymes, receptors, and compounds involved therein.The term "lipid metabolism cluster" or "lipid metabolism cluster" used herein refers to a group of markers (i.e., low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, choline, etc.) that are known to provide information about the current status of all biochemical and cellular events involved in lipid metabolism at a specific moment in a subject's life.

[0030] The term "inflammation" refers to the complex biological response of body tissues to harmful stimuli, such as pathogens or their particles, damaged cells, or irritants, and is a defensive reaction involving immune cells, blood vessels, and molecular mediators. The function of inflammation is to eliminate the initial cause of cellular injury, remove necrotic cells and damaged tissue from the original injury and inflammatory process, and initiate tissue repair. As used herein, the expression "inflammatory cluster" refers to a group of markers (i.e., levels of C-reactive protein (CRP), monocyte chemotactic protein-1 (MCP-1), etc.) known to provide information about the current state or status of biochemical and cellular events involved in the inflammatory process at a particular moment in a subject's life.

[0031] "Microbiota metabolism" refers to the many microbially derived small molecules present in a subject's gut microbiota (GM). The symbiotic relationship between the GM and the host generates numerous metabolic signatures, and technological advances in GM metabolomics are progressively deciphering host-microbe metabolic interactions. Thus, a "microbiota metabolic cluster" herein refers to a group of possible markers (i.e., succinate, lactate, TMA, etc.) in an isolated sample that provide information about the type and state of the microbiota. For a more detailed understanding of this term, see P., Del Chierico, F., & Putignani, L. (2020). Gut Microbiota Metabolism and Interaction with Food Components. International Journal of Molecular Sciences, 21(10), 3688. https: / / doi.org / 10.3390 / ijms21103688.

[0032] Under the term "oxidative stress," those skilled in the art will understand it as a condition reflecting an imbalance between the systemic manifestations of reactive oxygen species (ROS) and the ability of biological systems to readily detoxify reactive intermediates or repair the resulting damage. Disturbances in the normal redox state of cells can cause toxic effects through the production of peroxides and free radicals that damage all components of the cell, including proteins, lipids, and DNA. The "oxidative stress cluster" or "oxidative stress cluster" refers to a group of markers (i.e., levels of 8-isoprostaglandin F2α (8-iso-PGF2α), uric acid, dimethylglycine, etc.) known to provide information about the presence and extent of this imbalance at a particular moment in a subject's life.

[0033] As used herein, the term "subject" includes animals, more particularly mammals, and even more particularly humans.

[0034] As indicated previously, a first aspect of the present invention is a method for selecting a diet for a subject based on health data, comprising: (i) In an isolated sample of a subject, two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress determining two or more markers of (ii) assigning each cluster a score derived from a function that takes into account the levels and / or concentrations of two or more markers in the sample; (iii) comparing the scores obtained for two or more clusters (a) to (e) to determine the gradation between them; (iv) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score value; The present invention relates to a method, including:

[0035] In certain embodiments of the method of the first aspect, step (iii) comprises comparing the scores obtained for two or more of clusters (a)-(e) to determine a highest score, and step (iv) comprises recommending a diet for the subject taking into account said higher of the scores for two or more of clusters (a)-(e).

[0036] In another particular embodiment of the first aspect, the function for obtaining a score for each of the clusters analyzed in step (ii) comprises assigning to each of the markers of the cluster a value resulting from the level and / or concentration of the marker detected in the isolated sample modified by a factor (or weight).

[0037] In certain embodiments, the factor is a rational number between -1 and +1, with an absolute value between / 0 / and / 1 / , resulting from the relative significance of the marker within the entire set of markers in that cluster.

[0038] All this means that the level and / or concentration of each marker is multiplied by a value (factor or weight) according to the significance of that marker in the cluster.Therefore, the "significance" of the marker in the group of markers is finally given a numerical value (rational number), which is determined, for example, as shown in the following paragraph.Other methods, such as regression or variable selection methods, can be used to obtain numerical values, as known to those skilled in the art.

[0039] Calculation of the absolute value of the theoretical interpretation (-1 to +1) for each analyzed marker in a determined cluster can be performed, for example, by assigning a relative punctuation (individual marker score (SIM)) to each marker that plays a role in that cluster as a function of its significance within that cluster, where the significance is determined according to the inventors' observations, general knowledge about that marker in a particular metabolic pathway (i.e., from scientific evidence), and machine learning (ML) methods (e.g., PLS regression). Thus, punctuation is a quantitative measure of the significance of a marker in the group of markers that fit into the cluster. For example, we assign a punctuation of 25 to LDL. The remaining markers in lipid metabolism are assigned punctuations in the same way and under the same criteria. Therefore, the SIM value itself is an arbitrary number assigned to the marker. The important point here is that the relationship between the SIM values ​​of different biomarkers must be consistent (i.e., proportional) to the respective significance of each marker in the cluster.

[0040] In certain embodiments, the SIM value of each of two or more markers determined for a selected cluster is proportionally related to the SIM value of each other marker determined in the same cluster in proportions that fall within the ranges set forth in Tables F, G, H, I, and J, and X is selected from 0.75 to 0. In more particular embodiments, X is selected from 0.5 to 0. In even more particular embodiments, X is selected from 0.45 to 0, 0.40 to 0, or 0.35 to 0. In even more particular embodiments, X is selected from 0.30 to 0. In even more particular embodiments, X is selected from 0.25 to 0, 0.20 to 0, or 0.15 to 0. In even more particular embodiments, X is selected from 0.10 to 0 or 0.05 to 0. In more particular embodiments, X is selected from 0.75, 0.70, 0.65, 0.60, 0.55, 0.50, 0.45, 0.40, 0.35, 0.30, 0.25, 0.20, 0.19, 0.18, 0.17, 0.16, 0.15, 0.14, 0.13, 0.12, 0.11, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, or 0. In more particular embodiments, X is selected from 0.50, 0.45, 0.40, 0.35, 0.30, 0.25, 0.20, 0.19, 0.18, 0.17, 0.16, 0.15, 0.14, 0.13, 0.12, 0.11, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, or 0. In more particular embodiments, X is selected from 0.30, 0.25, 0.20, 0.19, 0.18, 0.17, 0.16, 0.15, 0.14, 0.13, 0.12, 0.11, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, or 0. In more particular embodiments, X is selected from 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, or 0. In more particular embodiments, X is 0.

[0041] In another particular embodiment, the SIM values ​​for each of the two or more markers determined for a selected cluster are ordered within each cluster according to the order in Tables A-E.

[0042] When punctuation is assigned to the marker that plays a role in that cluster, all punctuations are added, and each of them is divided by the total punctuation to obtain the weight or factor of the marker in that cluster.For example, in the previous example, LDL has 25 punctuations, if the total punctuation of the markers considered for the lipid metabolism cluster reaches 182 (that is, for simplicity, consider all other markers that are not listed here), the weight (percentage) of LDL in that cluster is 13.7% [(25 / 182)*100], or the same as the factor that increases the level of LDL determined in isolated samples, and has an absolute value of 0.137. The rational numbers can then be positive or negative, for example, by assigning negative values ​​to markers whose levels and / or concentrations in the assayed sample decrease in the presence of or at risk for a disease associated with that metabolic cluster (referred to in the table as "reverse"); and by assigning positive values ​​to markers whose levels and / or concentrations in the assayed sample increase in the presence of or at risk for a disease associated with that metabolic cluster (referred to in the table as "normal"). For example, since LDL levels are known to increase in the presence of diseases associated with lipid metabolism (e.g., obesity, atherosclerosis, etc.), the corresponding rational number from -1 to +1 to increase the determined level of LDL in the previous example would be (+0.137).

[0043] In certain embodiments of the first aspect, the weight or factor for a particular marker within a cluster, which provides a measure of the significance of the marker within the cluster, is calculated using formula (1):

number

[0044] In more particular embodiments, the SIM for each marker is as disclosed in the SIM(P) (Score (Punctuation) of Individual Marker) column of Tables A-E. In even more particular embodiments, the SIM for each marker is as disclosed in the SIM(P) (Score (Punctuation) of Individual Marker) column of Tables 1-5.

[0045] In some embodiments, the specific punctuation of each marker, and therefore the corresponding estimation factor within the markers of that cluster, can be adjusted as a function of the analyzed population. Thus, they can be customized to take into account some aspect of the analyzed cohort, such as race, geographic characteristics, specific diseases, etc. Or, they can be separated as a function of the different analyzed markers, each considered relative to the others.

[0046] As previously announced, the essence of the present invention lies in the combination of different biomarkers (i.e., at least two) that comprise or define a health signature, each marker being related to a slightly different aspect of metabolism, but representing the same signature. In other words, the essence lies in considering or converting different biomarkers (i.e., the levels of individualized markers in a sample) into a single composite biomarker (i.e., mathematically converted into a score for a specific cluster).

[0047] Thus, one skilled in the art will understand that there is no essentiality to these particular rational numbers (i.e., -1 to +1, or weight percent) that are ultimately used to increase the level of the marker in the function that derives the score for that cluster.

[0048] In another particular embodiment of the method, optionally in combination with the above or below embodiments, the function for obtaining the score for each cluster in step (ii) is the following formula (2):

number

[0049] In certain embodiments, the "weights (clusters)" in Equation (2) n " is calculated as above, particularly by using equation (1).

[0050] In more particular embodiments, the value assigned to each of the markers (i.e., the "biomarker (cluster)" in formula (2) above) is n ") is the quantity calculated as a Z-score value, and the Z-score of a marker is calculated by subtracting the reference population mean from the level and / or concentration and then dividing the difference by the standard deviation of the reference population.

[0051] Thus, in certain embodiments, the biomarker (cluster) n ZScore biomarker (cluster) ncan be called, and equation (3):

number

[0052] Those skilled in the art will recognize appropriate reference populations. For example, if a study is conducted using European subjects, a population representative of the general European population would be most appropriate to use to obtain such Z-scores that are calculated in the function to obtain cluster scores.

[0053] In certain embodiments, a method for selecting a diet for a subject based on health data comprises: (i) in an isolated sample of a subject, two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress determining two or more markers of (a) Markers of the carbohydrate metabolism cluster are selected from glucose, homeostasis model assessment of insulin resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine; (b) Markers of the lipid metabolism cluster are selected from LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin; (c) Markers of inflammation clusters were selected from C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor α (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFA), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation antigen 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA(total)); (d) markers of microbiota metabolic clusters were selected from trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide-binding protein (LBP), succinate, lactate, and acetate; (e) the marker of the oxidative stress cluster is selected from 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine; Steps and; (ii) the following formula (2):

number

number

[0054] In particular embodiments of the above, X is selected from 0.30 to 0. In more particular embodiments, X is selected from 0.20 to 0. In even more particular embodiments, X is selected from 0.10 to 0. In even more particular embodiments, X is 0.

[0055] In another particular embodiment of the first aspect, (a) the two or more markers of the carbohydrate metabolism cluster are selected from glucose, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine.

[0056] In another particular embodiment of the first aspect, (a) the two or more markers of the carbohydrate metabolism cluster are selected from glucose, homeostasis model assessment of insulin resistance (HOMA-IR), glutamate, leptin, adiponectin, valine, leucine, and isoleucine.

[0057] In yet another particular embodiment of the method of the first aspect, (b) the two or more markers of the lipid metabolism cluster are selected from LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin.

[0058] In another particular embodiment of the method of the first aspect, (b) the two or more markers of the lipid metabolism cluster are selected from LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, and total monounsaturated fatty acids (MUFA(total)).

[0059] In yet another particular embodiment of the method of the first aspect, (c) the two or more markers of an inflammatory cluster are selected from C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFAs), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA(total)).

[0060] In yet another particular embodiment of the method of the first aspect, (c) the two or more markers of an inflammatory cluster are selected from C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), and interleukin-10 (IL-10).

[0061] In yet another particular embodiment of the method of the first aspect, (d) the two or more markers of the microbiota metabolic cluster are selected from trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide binding protein (LBP), succinate, lactate, and acetate.

[0062] In yet another particular embodiment of the method of the first aspect, (d) the two or more markers of the microbiota metabolic cluster are selected from trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, and choline.

[0063] In yet another particular embodiment of the method of the first aspect, (e) the two or more markers of the oxidative stress cluster are selected from 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine.

[0064] In yet another particular embodiment of the method of the first aspect, (e) the two or more markers of the oxidative stress cluster are selected from 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), and oxidized low-density lipoprotein (LDLox).

[0065] In more detailed embodiments of the first aspect, in step (i), the following markers of cluster (a) are determined in the isolated sample: glucose, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine.

[0066] In yet another more detailed embodiment of the first aspect, in step (i), the following markers of cluster (b) are determined in the isolated sample: LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin.

[0067] In another more detailed embodiment of the first aspect, in step (i), the following markers of the (c) cluster are determined in the isolated sample: C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFA), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA(total)).

[0068] In another more detailed embodiment of the first aspect, in step (i), the following markers of the (d) cluster are determined in the isolated sample: trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide binding protein (LBP), succinate, lactate, and acetate.

[0069] In another more detailed embodiment of the first aspect, in step (i), the following markers of the (e) cluster are determined in the isolated sample: 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine.

[0070] The markers contemplated by the present invention can be determined according to methods that are well known and available to those skilled in the art.

[0071] In another particular embodiment of the method of the first aspect, it comprises identifying in the isolated sample in step (i) one, two, three, four, or five of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress The method includes determining two or more markers of

[0072] In another specific embodiment, two or more markers in (a) and two or more markers in (b); two or more markers in (a) and two or more markers in (c); two or more markers in (a) and two or more markers in (d); two or more markers in (a) and two or more markers in (e); two or more markers in (b) and two or more markers in (c); two or more markers in (b) and two or more markers in (d); two or more markers in (b) and two or more markers in (e); two or more markers in (c) and two or more markers in (d); two or more markers in (c) and two or more markers in (e); and two or more markers in (d) and two or more markers in (e).

[0073] In another specific embodiment, two or more markers from each of three of clusters (a), (b), and (c); (a), (b), and (d); (a), (b), and (e); (b), (c), and (d); (b), (c), and (e); (c), (d), and (e); and (a), (c), and (e).

[0074] In another specific embodiment, two or more markers of (a), two or more markers of (b), two or more markers of (c), and two or more markers of (d); two or more markers of (a), two or more markers of (b), two or more markers of (c), and two or more markers of (e); two or more markers of (b), two or more markers of (c), two or more markers of (d), and two or more markers of (e); two or more markers of (a), two or more markers of (c), two or more markers of (d), and two or more markers of (e); and two or more markers of (a), two or more markers of (b), two or more markers of (d), and two or more markers of (e).

[0075] In yet another more detailed embodiment of the method according to the first aspect, in step (i), (a) the following markers of the cluster are determined: glucose, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine; (b) The following markers of the cluster: LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin were determined in the isolated samples; (c) The following markers of the cluster: C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFA), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation antigen 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA (total)) were determined in the isolated samples; (d) The following markers of the cluster: trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide-binding protein (LBP), succinate, lactate, and acetate were determined in the isolated samples; (e) The following markers of the cluster: 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine are determined in the isolated samples.

[0076] In yet another particular embodiment of the method of the first aspect, it further comprises determining one or more clinical or subject parameters selected from the group consisting of sex, height, weight, race, blood pressure, waist circumference, body mass index (BMI), and fat mass, and optionally one or more genotype data.

[0077] Genotype data is also meant to be taken into account if certain mutations (e.g., single nucleotide polymorphisms SNPs) are present or any other data related to a genetic predisposition to certain risks of a disease or disorder, particularly metabolic disorders, is applicable to the subject.

[0078] In another particular embodiment of the first aspect, the isolated sample wherein the different markers are determined from a biological fluid, in particular from one or more of blood, plasma, serum, and urine.

[0079] Although the tables below indicate that certain markers are determined in certain sample types, one skilled in the art will understand that most of the markers can be measured in other sample types and will not consider it obligatory to analyze only markers in one sample type to obtain specific information therefrom.

[0080] In certain embodiments, markers in clusters (a), (b), and (c) are determined in an isolated sample of plasma or serum, and markers in (d) and (e) are determined in an isolated sample of plasma, serum, or urine.

[0081] Thus, in certain embodiments of the first aspect of the present invention, the markers of cluster (a) determined in plasma are selected from glucose and propionylcarnitine, or both; the markers of cluster (b) determined in plasma are selected from one or more, or all of LDL-cholesterol, total cholesterol, HDL-cholesterol, triglycerides, LPC (total), and propionylcarnitine, and combinations thereof; the markers of cluster (c) determined in plasma are selected from MCP-1, TNFα, IL-6, IL-10, sICAM1, LPC (total), LBP, sCD14, and combinations thereof, or all of them; the marker of cluster (d) determined in plasma is LBP; and the marker of cluster (e) determined in plasma is LDLox.

[0082] Thus, in certain embodiments of the first aspect of the invention, the markers of cluster (a) determined in serum are selected from glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, lactate, valine, leucine, isoleucine, phenylalanine, glutamine, combinations thereof, or all of them; and the markers of cluster (b) determined in serum are one or more of PUFA (total), saturated fatty acids, MUFA (total), linoleic acid, DHA, oleic acid, choline, 3-hydroxybutyrate, adiponectin, and combinations thereof; or all of them; the markers of cluster (c) determined in serum are selected from CRP, N-acetylglycoprotein, SFA, DHA, linoleic acid, PUFA, and combinations thereof, or all of them; the markers of cluster (d) determined in serum are selected from one or more of TMAO, choline, lactate, and combinations thereof, or all of them; and the markers of cluster (e) determined in serum are selected from one or more of uric acid, methionine, glycine, and combinations thereof, or all of them.

[0083] Similarly, in another specific embodiment, the markers of cluster (d) determined in urine are selected from one or more, or all of TMA, betaine, DMA, dimethylglycine, succinic acid, acetic acid, and combinations thereof; and the markers of cluster (e) determined in urine are selected from one or more, or all of 8-iso-PGF2α, 8-OHdGurine, allantoin, betaine, pseudouridine, dimethylglycine, and combinations thereof.

[0084] Tables A-E below show particular embodiments of clusters of markers in the isolated sample types in which they are measured and assigned. Thus, in particular embodiments of the methods of the invention, clusters (a)-(e) containing markers in the isolated samples shown are determined in step (i).

[0085] Table A - Carbohydrate Metabolism Clusters [Table 1] Table B - Lipid Metabolism Cluster [Table 2] Table C - Inflammatory Clusters [Table 3] Table D - Microbiota Clusters [Table 4] Table E - Oxidative Stress Clusters [Table 5]

[0086] A person skilled in the art would know which kit assays and tests are more suitable for the analysis of each marker in the cited isolated samples.

[0087] In certain embodiments, the method of the present invention is a computer-implemented method that includes at least one of the following steps (ii), (iii), and (iv): In more specific embodiments, the method includes at least steps (ii) and (iii); more specifically, the method includes at least steps (ii), (iii), and (iv).

[0088] In certain embodiments, step (ii) assigns to the cluster a score obtained from a function that takes into account the levels and / or concentrations of two or more markers determined in the sample as defined in step (i).

[0089] In certain embodiments, a computer-implemented method in which at least one of steps (ii) to (iv) is performed on a computer, the computer comprising a memory for storing program instructions and a processor coupled to the memory for causing at least one of steps (ii) to (iv) to be performed.

[0090] In a more detailed embodiment, a computer-implemented method comprises performing all of steps (ii) through (iv) of the method of the present invention.

[0091] In another particular embodiment of the method of the first aspect, in step (iv), a diet selected from one or more diets suitable for regulating one or more of the subject's carbohydrate metabolism, lipid metabolism, inflammation indicator status, the subject's microbiota metabolism, and oxidative stress indicator is recommended.

[0092] In other words, independent of the diet selected for each group known to those skilled in the art (i.e., nutritionists), the recommended diet is one that includes two or more dietary types to control one or more of the indicated metabolic clusters.

[0093] In even more particular embodiments, the method further comprises the step of administering the selected diet. Accordingly, there is provided a method for selecting and administering a diet to a subject based on health data, the method comprising: (i) in an isolated sample of a subject, two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress determining two or more markers of (ii) assigning each cluster a score derived from a function that takes into account the levels and / or concentrations of two or more markers in the sample; (iii) comparing the scores obtained for two or more clusters (a) to (e) to determine the gradation between them; (iv) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score; (v) administering (i.e., treating) the subject with the selected diet; Also disclosed herein is a method, including:

[0094] This method of selecting and administering a diet has as a particular embodiment all of the methods disclosed for selecting a diet of the first aspect and set out above.

[0095] Other specific embodiments of the method for selecting and administering a diet relate to the type of diet ultimately administered being selected from one or more diets suitable for regulating one or more of the subject's carbohydrate metabolism, lipid metabolism, inflammation indicator status, the subject's microbiota metabolism, and oxidative stress indicators.

[0096] A first additional aspect of the present invention is a method for producing a cellular membrane comprising the steps of: (i) clustering scores obtained from a function taking into account the levels and / or concentrations of two or more markers determined in the sample as defined in the first aspect; (ii) comparing the scores obtained for two or more clusters (a)-(e) to determine the gradation between them; and (iii) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score value. The present invention corresponds to a computer-implemented method including at least one of:

[0097] In particular embodiments, the computer-implemented method comprises at least steps (i) and (ii). In more particular embodiments, the computer-implemented method comprises at least steps (i), (ii), and (iii).

[0098] As shown, the second aspect of the present invention is - Two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress a database storing data on two or more markers determined in an isolated sample of a subject; - a memory for storing program instructions including program instructions capable of implementing: (i) a function for obtaining a metabolic cluster score from the levels and / or concentrations of two or more markers in a sample; (ii) a comparison of the scores to determine a gradation between the scores; and (iii) a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; a processor coupled to the database and memory, which, when executing the program instructions, causes a function to obtain a metabolic cluster score from the levels and / or concentrations of two or more markers in the sample, to compare the scores to determine a gradient therebetween, and to make a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; The present invention relates to a data processing system for selecting a subject's diet based on health data, comprising:

[0099] In certain embodiments, the system is adapted to carry out the method according to the first aspect and any of its disclosed specific embodiments.

[0100] Thus, in a more detailed embodiment of the system, the system performs step (ii) by comparing the scores and determining the higher of them; and performs step (iii) of the method of the first aspect by selecting a meal based on the higher of the scores.

[0101] In other words, the second aspect corresponds to a data processing system comprising means for performing the steps of the computer-implemented method defined in the first additional aspect.

[0102] In certain embodiments, the means comprises a database, a memory, and a processor as defined above.

[0103] In another particular embodiment, a system is adapted to perform a computer-implemented method according to the first additional aspect and any of its disclosed particular embodiments.

[0104] Practical applications of the methods, computer-implemented methods, and systems of the present invention include, among others, nutritional recommendations based on food groups and incorporating these recommendations into a food catalog. Another practical implementation of the methods, computer-implemented methods, and systems is incorporating the recommendations into specific software (e.g., web apps) for nutritionists and / or subjects. Another practical implementation of the methods, computer-implemented methods, and systems of the present invention is a dashboard (i.e., a visual display of data) or web page for obtaining information from a user (i.e., a subject) and ultimately informing the user (i.e., a subject) about personalized nutritional recommendations.

[0105] The system of the second aspect of the invention is improved by implementing artificial intelligence by means of a neuronal network containing information optimized to improve the performance of the method and system of the invention.

[0106] The in vitro method of the present invention provides nutritional recommendations or relevant information for a subject's dietary selection. In one embodiment, the method of the present invention further comprises the steps of (v) collecting information related to the recommendations and (vi) storing the information on a data carrier.

[0107] In the context of the present invention, a "data carrier" is understood to mean any medium, such as paper, that contains meaningful information data for dietary selection. For example, the carrier may include a storage medium such as a ROM, e.g., a CD-ROM or a semiconductor ROM, or a magnetic recording medium, e.g., a floppy disk or a hard disk. Furthermore, the carrier may be a carrier capable of transmitting data, such as an electrical or optical signal, which may be transmitted via an electrical or optical cable, or by radio or other means. If the diagnostic / prognostic data is embodied in a signal that can be directly transmitted by a cable or other device or means, the carrier may be constituted by such a cable or other device or means. Other carriers include USB devices and computer archives. Examples of suitable data carriers are paper, a CD, a USB, a computer archive in a PC, or an audio recording containing the same information.

[0108] A second additional aspect of the present invention corresponds to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method defined in the first and / or first additional aspect of the present invention.

[0109] A third additional aspect of the present invention corresponds to a computer readable data carrier having stored thereon a computer program according to the second additional aspect.

[0110] Throughout the specification and claims, the word "comprise" and variations of this word are not intended to exclude other technical features, additives, ingredients, or steps. Furthermore, the word "comprise" encompasses the case of "consisting of." Additional objects, advantages, and features of the present invention will become apparent to those skilled in the art upon examination of the description or may be learned by practice of the present invention. The following examples are provided by way of illustration and are not intended to limit the present invention. Furthermore, the present invention covers all possible combinations of the specific and preferred embodiments described herein.

[0111] Table F: Carbohydrate metabolism cluster: Percentage range between SIM values ​​of biomarkers. Here, "SIM(cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; "n" is one particular biomarker of the cluster; and "cluster" is the carbohydrate metabolism cluster. [Table 6] TIFF2025534901000012.tif230165TIFF2025534901000013.tif238165TIFF2025534901000014.tif163165

[0112] Table G: Lipid metabolism clusters: percentage ranges between SIM values ​​of biomarkers. Here, "SIM(cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; "n" is one particular biomarker of the cluster; and "cluster" is the lipid metabolism cluster. [Table 7] TIFF2025534901000016.tif240165TIFF2025534901000017.tif230165TIFF2025534901000018.tif237165TIFF2025534901000019.tif34165

[0113] Table H: Inflammation clusters: percentage range between SIM values ​​of biomarkers. Here, "SIM(cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; "n" is one particular biomarker of the cluster; and "cluster" is the inflammation cluster. [Table 8] TIFF2025534901000021.tif233165TIFF2025534901000022.tif202165

[0114] Table I: Microbiota clusters: percentage ranges between SIM values ​​of biomarkers. Here, "SIM(cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; "n" is one particular biomarker of the cluster; and "cluster" is the microbiota metabolic cluster. [Table 9] TIFF2025534901000024.tif86165

[0115] Table J: Oxidative stress cluster: percentage range between SIM values ​​of biomarkers. Here, "SIM(cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; "n" is one particular biomarker of the cluster; and "cluster" is the oxidative stress cluster. [Table 10] TIFF2025534901000026.tif196165 [Example]

[0116] Example 1. Scoring system using multivariate assessment of metabolism. Forty-nine metabolic biomarkers (i.e., markers) out of a total of 51, listed in Tables 1-5 below, were analyzed in 600 subjects (from Spain, Denmark, and the Netherlands).

[0117] The procedure for determining personalized nutrition recommendations was based on a linear combination of biomarkers and weights (i.e., factors) as shown in Tables 1-5. Factor values ​​were assigned according to the significance of each marker in a cluster. Significance was established by: i) selecting one or more biomarkers within a cluster that represent specific metabolic processes biologically relevant to the cluster (i.e., carbohydrate metabolism dysregulation, lipid metabolism dysregulation, oxidative stress, inflammation, and microbiota-related systemic changes); ii) using the remaining biomarkers to set a model by partial least squares regression (PLSR) to predict the concentration values ​​of the selected biomarkers; iii) calculating a variable importance in projection (VIP) score for each predictive biomarker; and iv) setting the factor value for each biomarker proportional to its VIP score in the PLSR model and in accordance with or taking into account literature studies conducted by experts in the field.

[0118] For each subject, a score was then calculated from the levels of the markers as shown in equation (2).

number

[0119] The methodology was validated in intervention studies with healthy and obese volunteers, which demonstrated the effectiveness of the method in improving health status according to the algorithm.

[0120] This method allowed us to identify the function of biomarkers under study, which are supported by a solid body of evidence but lack a consensus or established threshold for distinguishing between health and disease or altered metabolic states. This is a limitation of some markers, implying that individuals cannot be classified in absolute or binary terms as either present or absent from the phenotype. Therefore, we applied a strategy to classify individuals along a scale that deviates from healthy. This was typically done by defining the overall distribution of the biomarker in the tested population and then determining whether the individual fell into a higher, lower, or intermediate range. Because the association between a given biomarker and altered health status was already known, this approach provided initial information on whether the biomarker indicated a higher risk of developing a given phenotype.

[0121] Data were analyzed by principal component analysis (PCA). Plots were generated showing the representation of each subject by their respective values ​​of PC1 vs. PC2.

[0122] The detected distribution in PCA suggested that each metabolic score showed different trends across the entire study population and could therefore capture different metabolic signatures within the population (data not shown).

[0123] Example 2. Validation cohort and dietary selection implementation. The approach described in Example 1 was used to provide dietary recommendations in a single-blind, parallel, randomized, controlled nutritional intervention trial registered with clinicaltrials.gov (NCT04641559). Briefly, 193 volunteers were randomly assigned to either control or personalized intervention. Control subjects (n=55) received standard dietary recommendations. Volunteers in the personalized intervention groups (n=68 and n=70) were assigned to dietary plans targeting carbohydrate metabolism, lipid metabolism, microbiota, inflammation, or oxidative stress according to their higher metabolic scores calculated as described above.

[0124] The effects of specific dietary plans on all metabolic scores were analyzed as intention-to-treat by linear mixed models with time as a random effect and participant (two levels, baseline and 21 weeks) as a fixed effect, intervention group (control vs. carbohydrate, lipid, inflammation, oxidative stress, or microbiota), and their interactions with sex and age as covariates. Within-group comparisons were performed per protocol by paired Mann-Whitney or Student's t and unpaired comparisons when analyzed according to intention-to-treat.

[0125] The results are shown in Tables 6A to 6G.

[0126] Table 6A - Control diet [Table 16] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test Table 6B - Diet for Carbohydrate Management [Table 17] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test Table 6C - Diet for Inflammation Management [Table 18] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test Table 6D Diet for Lipid Management [Table 19] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test Table 6E: Diet for Microbiota Management [Table 20] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test Table 6F_Dietary strategies for managing oxidative stress [Table 21] *Indicates statistical significance (p value < 0.05) by Student's t-test or Mann-Whitney U test

[0127] Table 6G - Statistical analysis of the between-group stabilized control group as reference in a linear mixed model with subject as a random effect and time, age, and sex as fixed effects. P-values ​​for group and group x time interaction for each diet plan versus control group (CARB, INFL, LIPID, MB, OXIS) are shown in plain text. Overall p-values ​​for group, time, and their interaction were calculated by ANOVA in the linear mixed model and are indicated by two consecutive asterisks (**). [Table 22] CARB: carbohydrates; INFL: inflammation; LIPID: lipids; MB: microbiota; OXIS: oxidative stress; **: overall p-values ​​for group, time, and their interactions were calculated by ANOVA with a linear mixed model

[0128] From the data in Tables 6A-6G, the following conclusions apply: The carbohydrate subgroup showed a beneficial effect on adiponectin levels, which were maintained between visits 1 and 2, in contrast to the other dietary plans, which showed reduced levels. Similar results were observed for glutamate. Furthermore, this intervention was the only one to show significant decreases in the cyclic branched-chain amino acids valine, leucine, and isoleucine, as well as the aromatic amino acids phenylalanine and tyrosine. Overall, carbohydrate scores were significantly reduced by the carbohydrate-targeted dietary plan. Furthermore, microbiome scores improved, reflecting significant decreases in at least LBP, TMAO, and DMA. Other scores (i.e., inflammation, lipids, and oxidative stress) were consistent with the trends shown by control volunteers.

[0129] The inflammatory subgroup showed positive results compared with controls in inflammatory biomarkers, including increases in glycine and decreases in DMG, CRP, and NAG. These beneficial effects were reflected in statistically significant improvements in inflammation scores. Nevertheless, these results were accompanied by statistically significant increases in lipid scores, clearly reflecting, at least, significant increases in LDL levels and decreases in HDL levels, along with changes in other parameters. Anthropometric measurements were also adversely affected by the inflammatory intervention. Thus, fat mass significantly increased and lean mass decreased. Analysis of this subgroup demonstrates that the scoring system can capture both beneficial and harmful effects of the intervention, and that this particular intervention caused undesirable side effects on lipid metabolism. This is not a negative feature of the method at hand, but rather an indication of the need for follow-up and certain dietary adjustments. On the other hand, these data on the effects on lipid scores induced by the diet considered for the "inflammatory group" allow for the adjustment of the subjects' diet, in terms of combining diets taking into account scores from both the inflammatory and lipid metabolism clusters.

[0130] The lipid subgroup showed minimal changes with the diet. Thus, significant increases in circulating PUFA and MUFA and maintenance of oleic acid levels contrasted with increases in linoleic acid, leptin, and SFA. Consequently, no differences were detected between the lipid-targeting diet plan and the control group in any of the calculated metabolic scores.

[0131] The microbiota subgroups showed beneficial results compared to the control group in markers of microbiota status and related processes, such as pseudouridine, glycine, betaine, dimethylglycine, TMA, and DMA. Overall, these changes resulted in beneficial and significant decreases in microbiota scores relative to the control group.

[0132] The oxidative stress subgroup showed positive results in different biomarkers of oxidative stress compared with control volunteers. Thus, this intervention was the only one to significantly reduce urinary isoprostanes, the gold standard for oxidative stress, along with urinary 8-OH-dG levels. Furthermore, some biomarkers of microbiota status, such as DMG, TMAO, and DMA, also improved compared with the control group. Furthermore, this intervention reduced both systolic and diastolic blood pressure and showed a slight but significant trend toward a decrease in body weight and BMI, likely due to a loss of lean mass and a preservation of fat mass. This diet plan was the only one to reduce oxidative stress scores.

[0133] With the exception of the lipid-targeting diet, each intervention successfully reduced its respective metabolic score. These results were accompanied by beneficial changes in key biomarkers of current clinical interest for the various metabolic processes targeted by each intervention. Nevertheless, the inflammation-targeting diet induced a significant increase in lipid scores as an undesirable side effect.

[0134] Overall, the results of the metabolic score are consistent with both clinical outcomes and changes in food choices, and it is therefore plausible that the metabolic score proposed by this invention can capture different outcomes of nutritional interventions.

[0135] For completeness, the present description also discloses the following numbered embodiments: 1. A method for selecting a subject's diet based on health data, comprising: (i) In an isolated sample of a subject, two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress determining two or more markers of (ii) assigning each cluster a score derived from a function that takes into account the levels and / or concentrations of two or more markers in the sample; (iii) comparing the scores obtained for two or more of the clusters (a) to (e) to determine the gradation between them; (iv) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score value; A method comprising:

[0136] 2. The method of embodiment 1, wherein the function for obtaining a score for each cluster in step (ii) comprises assigning to each of the markers of the cluster a value resulting from the concentration of the marker detected in the isolated sample corrected by a factor, optionally calculated as a Z-score.

[0137] 3. In step (ii), the score of each cluster is calculated using the following formula (2):

number

[0138] 4. The factor is expressed by the following formula (1):

number

[0139] 5. The method of embodiment 4, wherein the SIM value of each marker is proportionally related to the SIM values ​​of other determined markers in the same cluster with proportions falling within the ranges shown in Tables F-J, and X is selected from 0.75, 0.50, 0.30, 0.10, or 0.

[0140] 6. (a) Markers of the carbohydrate metabolism cluster were selected from glucose, homeostasis model assessment of insulin resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine; (b) markers of the lipid metabolism cluster are selected from LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin; (c) Markers of inflammation clusters were selected from C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor α (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFA), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation antigen 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA (total); (d) markers of microbiota metabolic clusters were selected from trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide-binding protein (LBP), succinate, lactate, and acetate; (e) the marker of the oxidative stress cluster is selected from 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine; 6. The method of any one of embodiments 1 to 5.

[0141] 7. The method of any one of embodiments 1 to 6, wherein in step (i), the following markers of cluster (a) are determined in the isolated sample: glucose, homeostasis model assessment of insulin resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine.

[0142] 8. The method of any one of embodiments 1 to 7, wherein in step (i), the following markers of cluster (b) are determined in the isolated sample: LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin.

[0143] 9. The method of any one of embodiments 1 to 8, wherein in step (i), the following markers of cluster (c) are determined in the isolated sample: C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFAs), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFA(total)).

[0144] 10. The method of any one of embodiments 1 to 9, wherein in step (i), the following markers of the (d) cluster are determined in the isolated sample: trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide-binding protein (LBP), succinate, lactate, and acetate.

[0145] 11. The method of any one of embodiments 1 to 10, wherein in step (i), the following markers of the (e) cluster are determined in the isolated sample: 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine.

[0146] 12. The method of any one of embodiments 1 to 11, wherein markers of two clusters, or alternatively three clusters, or alternatively four clusters, are determined in step (i).

[0147] 13. The method of any one of embodiments 1 to 12, wherein markers of five clusters are determined in step (i).

[0148] 14. Two or more of the following metabolic clusters: (a) Carbohydrate metabolism; (b) lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) Oxidative stress a database storing data of two or more markers determined in an isolated sample of a subject; a memory storing program instructions including program instructions capable of implementing: (i) a function for obtaining a metabolic cluster score from the levels and / or concentrations of two or more markers in a sample; (ii) a comparison of the scores to determine a gradation between the scores; and (iii) a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; a processor coupled to the database and memory that, when executing the program instructions, causes a function to obtain a metabolic cluster score from the levels and / or concentrations of two or more markers in the sample, compare the scores to determine a gradient therebetween, and make a subject's dietary selection based on the metabolic cluster scores and the comparison thereof; A system for selecting a target diet based on health data, comprising:

[0149] 15. A system according to embodiment 14, adapted to carry out the method according to any one of embodiments 1 to 13.

[0150] References list Vernocchi, P., Del Chierico, F., & Putignani, L. (2020). Gut Microbiota Metabolism and Interaction with Food Components. International journal of molecular sciences, 21(10), 3688. https: / / doi.org / 10.3390 / ijms21103688 European Patent No. 3529379

Claims

1. 1. A method for selecting a subject's diet based on health data, comprising: (i) in an isolated sample of said subject, two or more of the following metabolic clusters: (a) carbohydrate metabolism; (b) Lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) oxidative stress determining two or more markers of (a) the markers of the carbohydrate metabolism cluster are selected from glucose, homeostasis model assessment of insulin resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine; (b) the markers of the lipid metabolism cluster are selected from LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin; (c) markers of inflammation clusters are selected from C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFAs), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFAs (total)); (d) the markers of the microbiota metabolic cluster are selected from trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide binding protein (LBP), succinate, lactate, and acetate; (e) the marker of the oxidative stress cluster is selected from 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine; Steps and (ii) the following formula (2): [Equation 1] (In the formula, "Biomarker(cluster)" is the level and / or concentration of a particular marker determined in a sample isolated for a particular cluster, said concentration optionally calculated as a Z-score; "n" is one particular biomarker of said cluster; "Ncluster" is the total amount of biomarkers in that particular cluster; and "weight(cluster)" is the following formula (1): [Equation 2] (In the formula, "n" and "Ncluster" are as defined above; "SIM (cluster)" is the score (punctuation) of an individual marker for a particular marker determined in a sample isolated for a particular cluster; the SIM value for each marker is proportionally related to the SIM values ​​of other determined markers in the same cluster, with proportions falling within the ranges shown in Tables F-J, where X is selected from 0.75 to 0. is a weight or factor selected from rational numbers between -1 and +1, calculated according to assigning each cluster a score calculated according to (iii) comparing the scores obtained for two or more of clusters (a)-(e) to determine a gradation therebetween; (iv) recommending a diet to the subject taking into account the gradation and, optionally, the individual score value relative to a reference score value; A method comprising:

2. 2. The method of claim 1, wherein X is selected from 0.50 to 0.

3. 3. The method of claim 1, wherein X is selected from 0.25 to 0.

4. 4. The method of any one of claims 1 to 3, wherein X is selected from 0.10 to 0.

5. 5. The method of claim 1, wherein X is 0.

6. 6. The method of any one of claims 1 to 5, wherein in step (i) the following markers of cluster (a) are determined in the isolated sample: glucose, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), glutamate, uric acid, leptin, adiponectin, insulin, tyrosine, propionylcarnitine, lactate, valine, leucine, isoleucine, phenylalanine, and glutamine.

7. 7. The method of any one of claims 1 to 6, wherein in step (i) the following markers of cluster (b) are determined in the isolated sample: LDL-cholesterol, total cholesterol, total polyunsaturated fatty acids (PUFA(total)), HDL-cholesterol, saturated fatty acids, triglycerides, total monounsaturated fatty acids (MUFA(total)), total lysophosphatidylcholine (LPC total), linoleic acid, docosahexaenoic acid (DHA), oleic acid, choline, 3-hydroxybutyrate, propionylcarnitine, adiponectin, and leptin.

8. 8. The method of any one of claims 1 to 7, wherein in step (i) the following markers of cluster (c) are determined in the isolated sample: C-reactive protein (CRP), N-acetylglycoprotein, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-10 (IL-10), saturated fatty acids (SFAs), soluble intercellular adhesion molecule-1 (sICAM1), total lysophosphatidylcholine (LPC Total), lipopolysaccharide-binding protein (LBP), docosahexaenoic acid (DHA), soluble cluster of differentiation 14 (sCD14), linoleic acid, and total polyunsaturated fatty acids (PUFAs (Total)).

9. 9. The method of any one of claims 1 to 8, wherein in step (i) the following markers of the (d) cluster are determined in the isolated sample: trimethylamine (TMA), trimethylamine N-oxide (TMAO), betaine, choline, dimethylamine (DMA), dimethylglycine, lipopolysaccharide binding protein (LBP), succinate, lactate, and acetate.

10. 10. The method of any one of claims 1 to 9, wherein in step (i) the following markers of the (e) cluster are determined in the isolated sample: 8-isoprostaglandin F2α (8-iso-PGF2α), 8-hydroxyguanosine (8-OHdG), oxidized low-density lipoprotein (LDLox), uric acid, allantoin, betaine, pseudouridine, dimethylglycine, methionine, and glycine.

11. 11. The method of any one of claims 1 to 10, wherein two clusters, or alternatively three clusters, or alternatively four clusters of markers are determined in step (i).

12. 12. The method of claim 1, wherein the markers of the five clusters are determined in step (i).

13. 13. A computer-implemented method comprising at least one of the following steps (ii), (iii) and (iv) of the method defined in any one of claims 1 to 12:

14. 14. A computer-implemented method according to claim 13, comprising steps (ii), (iii) and (iv) of the method defined in any one of claims 1 to 12.

15. 15. A computer-implemented method according to claim 13 or 14, wherein step (ii) is carried out using data determined on a sample isolated according to step (i) of the method defined in any one of claims 1 to 12.

16. A data processing system comprising means for performing steps (ii), (iii) and (iv) of the method of any one of claims 1 to 12.

17. The means is, Two or more of the following metabolic clusters: (a) carbohydrate metabolism; (b) Lipid metabolism; (c) inflammation; (d) microbiota metabolism; and (e) oxidative stress a database storing data of two or more markers determined in an isolated sample of said subject; a memory storing program instructions including program instructions capable of implementing: (i) a function that derives a score for the metabolic cluster from the levels and / or concentrations of the two or more markers in the sample; (ii) a comparison of the scores to determine a gradation between the scores; and (iii) a subject's diet selection based on the scores for the metabolic cluster and the comparison thereof; a processor coupled to the database and the memory, which, when executing the program instructions, causes the function to obtain scores for the metabolic clusters from the levels and / or concentrations of the two or more markers in the sample, compare the scores to determine a gradient therebetween, and make a dietary selection for the subject based on the scores for the metabolic clusters and the comparison thereof; The system of claim 16, comprising:

18. 13. A computer program comprising instructions which, when executed by a computer, cause said computer to carry out steps (ii), (iii) and (iv) of the method defined in any one of claims 1 to 12.

19. 20. A computer readable data carrier having stored thereon a computer program as defined in claim 18.

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